


import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowerCAmelCase			=				{
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= list(s_dict.keys()	)
		for key in keys:
				lowercase__				= R'''.*/layers_(\d+)'''
				lowercase__				= key
				if re.match(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):
						lowercase__				= re.sub(R'''layers_(\d+)'''		,							R'''block/\1/layer'''		,							SCREAMING_SNAKE_CASE	)

				lowercase__				= R'''(encoder|decoder)\/'''

				if re.match(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):
						lowercase__				= re.match(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	).groups()
						if groups[0] == "encoder":
								lowercase__				= re.sub(R'''/mlp/'''		,							R'''/1/mlp/'''		,							SCREAMING_SNAKE_CASE	)
								lowercase__				= re.sub(R'''/pre_mlp_layer_norm/'''		,							R'''/1/layer_norm/'''		,							SCREAMING_SNAKE_CASE	)

						elif groups[0] == "decoder":
								lowercase__				= re.sub(R'''/mlp/'''		,							R'''/2/mlp/'''		,							SCREAMING_SNAKE_CASE	)
								lowercase__				= re.sub(R'''/pre_mlp_layer_norm/'''		,							R'''/2/layer_norm/'''		,							SCREAMING_SNAKE_CASE	)

        # 2. Convert other classic mappings
				for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
						if old_key in new_key:
								lowercase__				= new_key.replace(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)

				print(f'{key} -> {new_key}'	)
				lowercase__				= s_dict.pop(SCREAMING_SNAKE_CASE	)

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				lowercase__				= s_dict[
				    '''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
				].T
		if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				lowercase__				= s_dict[
				    '''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
				].T

		# 3. Take extra care of the EXPERTS layer
		for key in list(s_dict.keys()	):
				if "expert" in key:
						lowercase__				= s_dict[key].shape[0]
						lowercase__				= s_dict[key]
						for idx in range(SCREAMING_SNAKE_CASE	):
								lowercase__				= expert_weihts[idx]
								print(f'{key} -> {key.replace("expert/"		,							"nested fstring"	)}'	)

						s_dict.pop(SCREAMING_SNAKE_CASE	)

		return s_dict


lowerCAmelCase			=				{
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		import regex as re

		with open(SCREAMING_SNAKE_CASE		,							'''r'''	) as f:
				lowercase__				= f.read()

		lowercase__				= re.findall(R'''(.*) = ([0-9.]*)'''		,							SCREAMING_SNAKE_CASE	)
		lowercase__				= {}
		for param, value in regex_match:
				if param in GIN_TO_CONFIG_MAPPING and value != "":
						lowercase__				= float(SCREAMING_SNAKE_CASE	) if '''.''' in value else int(SCREAMING_SNAKE_CASE	)

		lowercase__				= re.findall(R'''(.*activations) = \(\'(.*)\',\)'''		,							SCREAMING_SNAKE_CASE	)[0]
		lowercase__				= str(activation[1]	)

		lowercase__				= num_experts
		lowercase__				= SwitchTransformersConfig(**SCREAMING_SNAKE_CASE	)
		return config
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE="./"		,							SCREAMING_SNAKE_CASE=8	):


		"""simple docstring"""




		print(f'Loading flax weights from : {flax_checkpoint_path}'	)
		lowercase__				= checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE	)

		if gin_file is not None:
				lowercase__				= convert_gin_to_config(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		else:
				lowercase__				= SwitchTransformersConfig.from_pretrained(SCREAMING_SNAKE_CASE	)

		lowercase__				= SwitchTransformersForConditionalGeneration(SCREAMING_SNAKE_CASE	)

		lowercase__				= flax_params['''target''']
		lowercase__				= flatten_dict(SCREAMING_SNAKE_CASE		,							sep='''/'''	)
		lowercase__				= rename_keys(SCREAMING_SNAKE_CASE	)
		lowercase__				= unflatten_dict(SCREAMING_SNAKE_CASE		,							sep='''/'''	)

		# Load the flax params in the PT model
		load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)

		print(f'Save PyTorch model to {pytorch_dump_path}'	)
		pt_model.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
			lowerCAmelCase			=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--switch_t5x_checkpoint_path',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
			        ' model architecture. If not provided, a `gin_file` has to be provided.'
			    ),
			)
			parser.add_argument(
			    '--gin_file',
			    default=None,
			    type=str,
			    required=False,
			    help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
			)
			parser.add_argument(
			    '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
			)
			parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
			lowerCAmelCase			=				parser.parse_args()
			convert_flax_checkpoint_to_pytorch(
			    args.switch_tax_checkpoint_path,
			    args.config_name,
			    args.gin_file,
			    args.pytorch_dump_folder_path,
			    args.num_experts,
			)






"""simple docstring"""



import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class    __a						( __snake_case					,			unittest.TestCase							):
				lowerCamelCase     :       Any			       =GPTSanJapaneseTokenizer
				lowerCamelCase     :       List[str]			       =False
				lowerCamelCase     :       List[str]			       ={'do_clean_text': False, 'add_prefix_space': False}







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					super().setUp()

					# fmt: off
					lowerCAmelCase_      							=			['''こん''', '''こんに''', '''にちは''', '''ばんは''', '''世界,㔺界''', '''、''', '''。''', '''<BR>''', '''<SP>''', '''<TAB>''', '''<URL>''', '''<EMAIL>''', '''<TEL>''', '''<DATE>''', '''<PRICE>''', '''<BLOCK>''', '''<KIGOU>''', '''<U2000U2BFF>''', '''<|emoji1|>''', '''<unk>''', '''<|bagoftoken|>''', '''<|endoftext|>''']
					# fmt: on
					lowerCAmelCase_      							=			{'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
					lowerCAmelCase_      							=			{'''unk_token''': '''<unk>'''}

					lowerCAmelCase_      							=			os.path.join(self.tmpdirname      ,	VOCAB_FILES_NAMES['''vocab_file''']   )
					lowerCAmelCase_      							=			os.path.join(self.tmpdirname      ,	VOCAB_FILES_NAMES['''emoji_file''']   )
					with open(self.vocab_file      ,	'''w'''      ,	encoding='''utf-8'''   ) as vocab_writer:
						vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )
					with open(self.emoji_file      ,	'''w'''   ) as emoji_writer:
						emoji_writer.write(json.dumps(UpperCAmelCase   )   )







				def      lowerCamelCase_     ( self      ,	**UpperCAmelCase   ):

					'''simple docstring'''






					kwargs.update(self.special_tokens_map   )
					return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname      ,	**UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''こんにちは、世界。 \nこんばんは、㔺界。😀'''
					lowerCAmelCase_      							=			'''こんにちは、世界。 \nこんばんは、世界。😀'''
					return input_text, output_text







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.get_input_output_texts(UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.encode(UpperCAmelCase      ,	add_special_tokens=UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase      ,	clean_up_tokenization_spaces=UpperCAmelCase   )
					return text, ids







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass  # TODO add if relevant







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass  # TODO add if relevant







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass  # TODO add if relevant







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.get_tokenizer()

					# Testing tokenization
					lowerCAmelCase_      							=			'''こんにちは、世界。　こんばんは、㔺界。'''
					lowerCAmelCase_      							=			['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
					lowerCAmelCase_      							=			tokenizer.tokenize(UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )

					# Testing conversion to ids without special tokens
					lowerCAmelCase_      							=			[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
					lowerCAmelCase_      							=			tokenizer.convert_tokens_to_ids(UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )

					# Testing conversion to ids with special tokens
					lowerCAmelCase_      							=			tokens + [tokenizer.unk_token]
					lowerCAmelCase_      							=			[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
					lowerCAmelCase_      							=			tokenizer.convert_tokens_to_ids(UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.get_tokenizer()

					# Testing tokenization
					lowerCAmelCase_      							=			'''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
					lowerCAmelCase_      							=			'''こんにちは、、、、世界。こんばんは、、、、世界。'''
					lowerCAmelCase_      							=			tokenizer.encode(UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

					# Testing tokenization
					lowerCAmelCase_      							=			'''こんにちは、世界。'''
					lowerCAmelCase_      							=			'''こんばんは、㔺界。😀'''
					lowerCAmelCase_      							=			'''こんにちは、世界。こんばんは、世界。😀'''
					lowerCAmelCase_      							=			tokenizer.encode(prefix_text + input_text   )
					lowerCAmelCase_      							=			tokenizer.encode(''''''      ,	prefix_text=prefix_text + input_text   )
					lowerCAmelCase_      							=			tokenizer.encode(UpperCAmelCase      ,	prefix_text=UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.decode(UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

					# Testing tokenization
					lowerCAmelCase_      							=			'''こんにちは、世界。'''
					lowerCAmelCase_      							=			'''こんばんは、㔺界。😀'''

					lowerCAmelCase_      							=			len(tokenizer.encode(UpperCAmelCase   )   ) - 2
					lowerCAmelCase_      							=			len(tokenizer.encode(UpperCAmelCase   )   ) - 2

					lowerCAmelCase_      							=			[1] + [0] * (len_prefix + len_text + 1)
					lowerCAmelCase_      							=			[1] * (len_prefix + len_text + 1) + [0]
					lowerCAmelCase_      							=			[1] + [1] * (len_prefix) + [0] * (len_text + 1)

					lowerCAmelCase_      							=			tokenizer(prefix_text + input_text   ).token_type_ids
					lowerCAmelCase_      							=			tokenizer(''''''      ,	prefix_text=prefix_text + input_text   ).token_type_ids
					lowerCAmelCase_      							=			tokenizer(UpperCAmelCase      ,	prefix_text=UpperCAmelCase   ).token_type_ids
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertListEqual(UpperCAmelCase      ,	UpperCAmelCase   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

					lowerCAmelCase_      							=			tokenizer.encode('''あンいワ'''   )
					lowerCAmelCase_      							=			tokenizer.encode(''''''      ,	prefix_text='''あンいワ'''   )
					lowerCAmelCase_      							=			tokenizer.encode('''いワ'''      ,	prefix_text='''あン'''   )

					self.assertEqual(tokenizer.decode(UpperCAmelCase   )      ,	tokenizer.decode(UpperCAmelCase   )   )
					self.assertEqual(tokenizer.decode(UpperCAmelCase   )      ,	tokenizer.decode(UpperCAmelCase   )   )
					self.assertNotEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertNotEqual(UpperCAmelCase      ,	UpperCAmelCase   )
					self.assertEqual(x_token_a[1]      ,	x_token_a[-1]   )  # SEG token
					self.assertEqual(x_token_a[1]      ,	x_token_a[3]   )  # SEG token







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''   )

					lowerCAmelCase_      							=			[['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
					lowerCAmelCase_      							=			tokenizer(UpperCAmelCase      ,	padding=UpperCAmelCase   )
					lowerCAmelCase_      							=			tokenizer.batch_encode_plus(UpperCAmelCase      ,	padding=UpperCAmelCase   )

					# fmt: off
					lowerCAmelCase_      							=			[[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
					lowerCAmelCase_      							=			[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
					lowerCAmelCase_      							=			[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
					# fmt: on
					self.assertListEqual(x_token.input_ids      ,	UpperCAmelCase   )
					self.assertListEqual(x_token.token_type_ids      ,	UpperCAmelCase   )
					self.assertListEqual(x_token.attention_mask      ,	UpperCAmelCase   )
					self.assertListEqual(x_token_a.input_ids      ,	UpperCAmelCase   )
					self.assertListEqual(x_token_a.token_type_ids      ,	UpperCAmelCase   )
					self.assertListEqual(x_token_a.attention_mask      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass